A generalised label noise model for classification in the presence of annotation errors

© 2016 Elsevier B.V. Supervised learning from annotated data is becoming more challenging due to inherent imperfection of training labels. Previous studies of learning in the presence of label noise have been focused on label noise which occurs randomly, while the study of label noise that is influe...

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Main Author: Jakramate Bootkrajang
Format: Journal
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/55519
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-555192018-09-05T03:11:39Z A generalised label noise model for classification in the presence of annotation errors Jakramate Bootkrajang Computer Science Neuroscience © 2016 Elsevier B.V. Supervised learning from annotated data is becoming more challenging due to inherent imperfection of training labels. Previous studies of learning in the presence of label noise have been focused on label noise which occurs randomly, while the study of label noise that is influenced by input features, which is intuitively more realistic, is still lacking. In this paper, we propose a new, generalised label noise model which is able to withstand the negative effect of random label noise and a wide range of non-random label noises. Empirical studies using a battery of synthetic data and four real-world datasets with inherent annotation errors demonstrate that the proposed generalised label noise model improves, in terms of classification accuracy, upon existing label noise modelling approaches. 2018-09-05T02:57:27Z 2018-09-05T02:57:27Z 2016-06-05 Journal 18728286 09252312 2-s2.0-84959469626 10.1016/j.neucom.2015.12.106 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84959469626&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55519
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Neuroscience
spellingShingle Computer Science
Neuroscience
Jakramate Bootkrajang
A generalised label noise model for classification in the presence of annotation errors
description © 2016 Elsevier B.V. Supervised learning from annotated data is becoming more challenging due to inherent imperfection of training labels. Previous studies of learning in the presence of label noise have been focused on label noise which occurs randomly, while the study of label noise that is influenced by input features, which is intuitively more realistic, is still lacking. In this paper, we propose a new, generalised label noise model which is able to withstand the negative effect of random label noise and a wide range of non-random label noises. Empirical studies using a battery of synthetic data and four real-world datasets with inherent annotation errors demonstrate that the proposed generalised label noise model improves, in terms of classification accuracy, upon existing label noise modelling approaches.
format Journal
author Jakramate Bootkrajang
author_facet Jakramate Bootkrajang
author_sort Jakramate Bootkrajang
title A generalised label noise model for classification in the presence of annotation errors
title_short A generalised label noise model for classification in the presence of annotation errors
title_full A generalised label noise model for classification in the presence of annotation errors
title_fullStr A generalised label noise model for classification in the presence of annotation errors
title_full_unstemmed A generalised label noise model for classification in the presence of annotation errors
title_sort generalised label noise model for classification in the presence of annotation errors
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84959469626&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/55519
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